Computational Social Science
Computational Social Science
Discovery and Prediction
Alvarez, R. Michael
Cambridge University Press
03/2016
337
Mole
Inglês
9781107518414
500
Descrição não disponível.
Preface Gary King; Introduction R. Michael Alvarez; Part I. Computation Social Science Tools: 1. The application of big data in surveys to the study of public opinion, elections, and representation Christopher Warshaw; 2. Navigating the local modes of big data: the case of topic models Margaret Roberts, Brandon Stewart and Dustin Tingley; 3. Generating political event data in near real time: opportunities and challenges John Beieler, Patrick T. Brandt, Andrew Halterman, Philip A. Schrodt and Erin M. Simpson; 4. Network structure and social outcomes: network analysis for social science Betsy Sinclair; 5. Ideological salience in multiple dimensions Peter Foley; 6. Random forest applied to feature selection in biomedical research Daniel Conn and Christina Ramirez; Part II. Computation Social Science Applications: 7. Big data, social media, and protest: foundations for a research agenda Joshua Tucker, Jonathan Nagler, Megan Metzger, Pablo Barbera, Duncan Penfold-Brown, John Jost and Richard Bonneau; 8. Measuring representational style in the House: the Tea Party, Obama and legislators' changing expressed priorities Justin Grimmer; 9. Using social marketing and data science to make government smarter Brian Griepentrog, Sean Marsh, Sidney Carl Turner and Sarah Evans; 10. Using machine algorithms to detect election fraud Ines Levin, Julia Pomares and R. Michael Alvarez; 11. Centralized analysis of local data, with dollars and lives on the line: lessons from the home radon experience Phillip N. Price and Andrew Gelman; Conclusion. Computational social science: towards a collaborative future Hanna Wallach.
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Preface Gary King; Introduction R. Michael Alvarez; Part I. Computation Social Science Tools: 1. The application of big data in surveys to the study of public opinion, elections, and representation Christopher Warshaw; 2. Navigating the local modes of big data: the case of topic models Margaret Roberts, Brandon Stewart and Dustin Tingley; 3. Generating political event data in near real time: opportunities and challenges John Beieler, Patrick T. Brandt, Andrew Halterman, Philip A. Schrodt and Erin M. Simpson; 4. Network structure and social outcomes: network analysis for social science Betsy Sinclair; 5. Ideological salience in multiple dimensions Peter Foley; 6. Random forest applied to feature selection in biomedical research Daniel Conn and Christina Ramirez; Part II. Computation Social Science Applications: 7. Big data, social media, and protest: foundations for a research agenda Joshua Tucker, Jonathan Nagler, Megan Metzger, Pablo Barbera, Duncan Penfold-Brown, John Jost and Richard Bonneau; 8. Measuring representational style in the House: the Tea Party, Obama and legislators' changing expressed priorities Justin Grimmer; 9. Using social marketing and data science to make government smarter Brian Griepentrog, Sean Marsh, Sidney Carl Turner and Sarah Evans; 10. Using machine algorithms to detect election fraud Ines Levin, Julia Pomares and R. Michael Alvarez; 11. Centralized analysis of local data, with dollars and lives on the line: lessons from the home radon experience Phillip N. Price and Andrew Gelman; Conclusion. Computational social science: towards a collaborative future Hanna Wallach.
Este título pertence ao(s) assunto(s) indicados(s). Para ver outros títulos clique no assunto desejado.
